本科生在增强现实辅助科学学习中的认知负载概况及其与科学的关系 学习自我效能和行为模式
Xiao-Fan Lin1,2,3, Seng Yue Wong4, Wei Zhou1
1School of Education Information Technology, Office 214, GuangDong Engineering Technology Research Center of Smart Learning, South China Normal University, 55 Zhongshan Dadao Xi, Guangzhou, 510631 China.
International journal of science and mathematics education
|June 26, 2023
概括
科学学习中的增强现实 (AR) 在学生中创造了多样化的认知负载配置文件. 定制AR策略以学生的个人资料,如沉浸式或有组织,是提高自我效能和学习成果的关键.
科学领域:
- 教育技术的教育技术
- 认知科学 认知科学
- 科学教育科学教育教育.
背景情况:
- 由于个人的认知负载差异,增强现实 (AR) 辅助学习的有效性有所不同.
- 了解学生的认知负载概况对于优化AR科学学习设计至关重要.
- 之前的研究表明,需要探索与AR有效性相关的学生特征.
研究的目的:
- 用AR用于科学学习的本科生识别不同的认知负载概况.
- 检查这些个人资料,自我效能和行为模式之间的关系.
- 为设计更有效的AR辅助科学学习干预措施提供信息.
主要方法:
- 隐性个人资料分析 (LPA) 用于根据认知负载尺寸对学生进行分类.
- 多变量差异分析 (MANOVA) 通过对已识别的个人资料进行自我有效性水平的比较.
- 滞后顺序分析检查了个人资料之间学习行为模式的差异.
主要成果:
- 他们确定了四种不同的认知负载概况:低参与度,沉浸式,跳动和有组织.
- 沉浸式的个人资料显示了最高的科学学习自我效能,而低参与显示了最低的.
- 参与社交互动,测试和反审查的学生表现出比那些专注于资源访问的学生更高的自我效能.
结论:
- 学生的认知负载概况在AR科学学习中显著影响自我效能和行为.
- 个性化的AR干预,与特定的学生个人资料相匹配,可以提高学习成果.
- 识别学生的不同需求对于开发有效的AR辅助教育工具至关重要.
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